We demonstrate that STML is also effective for
metric learning from scratch without pre-training on ImageNet.
Following [1]}, we employ randomly initialized ResNet18 [2]} with 128 embedding dimensions as the backbone network, and evaluate our model trained on the SOP dataset.
As shown in Table REF , our model substantially outperforms existing methods including those for self-supervised representation learning in this setting.
This result suggests that STML can generate reliable pseudo labels even on a randomly initialized embedding space, and our model trained by STML well generalizes to unseen class.
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